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Activity Number:
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350
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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Abstract #319701
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Title:
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Functional Divergence-Based Classification for Single-Cell Data
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Author(s):
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Ollivier Hyrien*
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Companies:
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University of Rochester
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Keywords:
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supervised classification ;
flow cytometry ;
divergences ;
nonparametric
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Abstract:
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Single-cell assays are routinely used in clinical settings for disease diagnosis and prognosis. The construction of supervised classifiers in this context often begins by extracting candidate features from the data from which a decision rule is subsequently constructed. We propose a different approach to classification in which we perform class assignment by summarizing the evidence provided by the data that the subject belongs to each class by means of divergences. The proposed approach makes no distributional assumptions about phenotypes. It automatically integrates predictive patterns in the classifier, eliminating the construction and selection of candidate features from the process. The finite sample performances of the approach are studied in simulations. An application to flow cytometry data is also presented.
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Authors who are presenting talks have a * after their name.
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